Artist Statement and Pittsburgh Dataset Project

By Jena J

January 30, 2019

For my essay, I wrote about the abundance of restaurants on Pitt’s campus and the necessity for health and safety inspections at those restaurants. As a Pitt student who spends most of my time on campus and lives in Oakland, I know just how frustrating it can be to take a bus to get groceries. While there is a pop-up store on campus now, I find the prices a bit inflated and I also think that the store doesn’t offer everything I’d like. I, personally, try not to eat out too much because it can also start to get expensive after a while, but I can understand the appeal of doing so. As someone who feels like they’re always on the run, it’s easier sometimes to just place an online order for a restaurant on campus and pick it up as I’m running.

I stumbled across this dataset, and it made me start to wonder where health and safety inspections originated, as well as what the data might say about the sanitation of restaurants, not only in Oakland, but throughout the rest of Pittsburgh as well.

My initial goal in searching through the data was to analyze the violations incurred at restaurants by municipality as well as the types of violations that were incurred. As I was sifting through the data, however, I discovered that there was simply too much data remaining to create a comprehensible visualization. Realizing this, I decided to make my search more specific and began looking at restaurants in the Oakland zip code that had incurred multiple infractions over the past five years.

The data was a little difficult to navigate at first because the severity of an infraction was only denoted by “low,” “medium,” or “high,” and the only indication that a restaurant had incurred a violation of that severity was through a series of “true” and “false,” only denoted as T or F. The key for the data didn’t make this clear, but I was later able to infer that this was the case, so I decided to narrow down my results even more by focusing on only repeated critical violations.

For the visualization, I decided that it would be most effective to present my findings in a plot.ly bar graph that would depict the number of critical violations incurred per year. I created a table listing the nine restaurants with recurrent critical violations and set up different traces to show the numbers of violations that were incurred per year.

To integrate my plot.ly graph on my GitHub page, I decided to copy the html source and past it into my html to embed the graph on my page.

I would still be interested to look at violations as they’ve been incurred by municipality, but there’s way too much data at the moment, so I think I’ll need to think about how to best narrow down the results to visualize the data properly. If I can figure this out, I think I might like to create a Tableau map later in the semester that depicts the severity of violations per municipality. For now, I’ve discussed the history of health and safety inspections and the interest that Pitt students might take in those for the restaurants near them. You can read my essay and view the bar graph by visiting my GitHub Pages site.